Tutorial:
Graphical Models and Bayesian
Networks with R

Goals

Introduce participants to using R for working with graphical
models (in particular graphical log-linear models for discrete data (contingency
tables)) and to probability propagation in Bayesian networks.

Outline

There will be a running example about building a
probabilistic expert system for a medical diagnosis from real-world
data.

Probability propagation with Bayesian networks (BNs)
and their implementation in the gRain (gRaphical independence
networks) package.

A look under the hood of BNs to
understand mechanisms of probability
propagation.